Machine Learning and Investment Analytics for Trading

Methods for developing trading strategies based on AI and ML, both in short-term and longer-term investing, are gaining popularity.

AI and ML are being used to develop trading strategies, develop liquidity searching algorithms, risk management, defining and assessing credit ratings, predicting defaults, hedging overlays, portfolio clustering, and portfolio recommendations.

This is being achieved by using historical data for autonomous predictive modeling, external factors, SME overlays, and visualization with allowances for adjustments. This results in consistent forecast scenarios with consideration for every significant predictive factor including:

• Macro Factors/Fundamentals
• Competitive Intensity, Valuations, Industry Analysis
• Credit Fundamentals
• Volatility, Spread Dispersion, Default Rates, Credit Risk
• Asset Class Volatility
• News, Social Media, and Commentary through Machine Learning and NLP

By harnessing the vast amounts of structured and unstructured data available, these technologies enable more nuanced and dynamic decision-making processes that can adapt to changing market conditions.

• Macro factors and Fundamentals.
• Massive volumes of historical trading data, volatility etc.
• SME and Interview knowledge across the firm.
• Stock, Investment and Company Attribute data.
• News, Social Media and other Unstructured Data.

• Workshops with Investment SMEs to understand predictors/features for Machine Learning and Modelling.
• Create and test predictive model with high level of accuracy.
• Calculate indices/scores based on Natural Language Processing (NLP) & Sentiment Analytics for Unstructured Data.
• Forecast recommendations with the flexibility to incorporate Board, Exec. and SME/Trader overlays.

• Reduce recommendation & analytics effort/time by up to 80%.
• Increase recommendation accuracy and drive consistency between departments and fund owners.
• Predictions on a stock basis or any level of required detail for maximum forecast transparency and analytics.
• Ability to overlay Board, Executive targets and safety margins.
• Ability to overlay SME/Trader adjustments and perspectives.
• Consider every factor in your forecast for increased accuracy.